This paper derives necessary and sufficient conditions for nonparametric transformation models to be (i) correctly speci fied, and (ii) identi fied. Our correct speci fication conditions come in a form of partial diff erential equations; when satis fied by the true distribution, they ensure that...

In situations where a sequence of forecasts is observed, a common strategy is to examine ‘rationality’ conditional on a given loss function. We examine this from a different perspective - supposing that we have a family of loss functions indexed by unknown shape parameters, then given the...

This paper proposes a method for comparing and combining conditional quantile forecasts in an out-of-sample framework. We construct a Conditional Quantile Forecast Encompassing (CQFE) test as a Wald-type test of superior predictive ability. Rejection of CQFE provides a basis for combination of...

In this paper, we propose a new family of multivariate loss functions that can be used to test the rationality of vector forecasts without assuming independence across individual variables. When only one variable is of interest, the loss function reduces to the flexible asymmetric family...

Theoretical literature in finance has shown that the risk of financial time series can be well quantified by their expected shortfall, also known as the tail value-at-risk. In this paper, I construct a parametric estimator for the expected shortfall based on a flexible family of densities,...

Though one of the pillars of the theory of international trade, the extreme predictions of the Ricardian model have made it unsuitable for empirical purposes. A seminal contribution of Eaton and Kortum (2002) is to demonstrate that random productivity shocks are sufficient to make the Ricardian...

We derive the semiparametric efficiency bound in dynamic models of conditional quantiles under a sole strong mixing assumption. We also provide an expression of Stein’s (1956) least favorable parametric submodel. Our approach is as follows: First, we construct a fully parametric submodel of...